PeerJ Computer Science Preprints: Spatial and Geographic Information Systemshttps://peerj.com/preprints/index.atom?journal=cs&subject=11700Spatial and Geographic Information Systems articles published in PeerJ Computer Science PreprintsA novel shiny platform for the geo-spatial analysis of large amount of patient data.https://peerj.com/preprints/33352017-10-102017-10-10Francesco GuarinoMario Alessandro RussoMonica FranzeseDario RighelliGiovanni ImprotaClaudia AngeliniMaria Triassi
It has been estimated that up to 80% of all data stored in health care databases may have spatial components. To fully exploit such components, there is a need of improving existing tools or developing novel spatio-temporal functionalities. Geographic information systems (GIS) as QuantumGis, SOLAP2, etc. are potential candidates to support decisional needs, but despite their capabilities, they are still scarcely employed in association within BI applications. For these reasons, we are developing a GIS user-friendly interface in R environment in order to dynamically and interactively visualize and analyze (within BI platforms) diverse informative data layers (e.g., pathology incidence data, environmental pollution, etc.). Although preliminary, we believe that this kind of tools could be suitable used for epidemiologic, environmental and economical studies by providing geographical maps and statistical data analyses of interest for different stakeholders.

It has been estimated that up to 80% of all data stored in health care databases may have spatial components. To fully exploit such components, there is a need of improving existing tools or developing novel spatio-temporal functionalities. Geographic information systems (GIS) as QuantumGis, SOLAP2, etc. are potential candidates to support decisional needs, but despite their capabilities, they are still scarcely employed in association within BI applications. For these reasons, we are developing a GIS user-friendly interface in R environment in order to dynamically and interactively visualize and analyze (within BI platforms) diverse informative data layers (e.g., pathology incidence data, environmental pollution, etc.). Although preliminary, we believe that this kind of tools could be suitable used for epidemiologic, environmental and economical studies by providing geographical maps and statistical data analyses of interest for different stakeholders.

Probabilistic graph models for landscape geneticshttps://peerj.com/preprints/22252017-10-012017-10-01Brook G. Milligan
Landscape genetics combines population genetics, landscape ecology, and spatial analysis to identify landscape and genetic factors that influence genetic and genomic variation. Progress in the field depends on a strong conceptual foundation and the means of identifying mechanistic connnections between environmental factors, landscape features, and genetic or genomic variation. Many existing approaches and much of the software commonly in use was developed for population genetics or statistics and is not entirely appropriate for landscape genetics. Probabilistic graph models provide a statistically rigorous and flexible means of constructing models directly applicable to landscape genetics. Probabilistic graph models also allow construction of mechanistic models, which are crucial elements in testing hypotheses. Sophisticated software exists for the analysis of graph models; however, much of it does not handle the types of data used for landscape genetics, model structures involving autoregressive spatial interaction between variables, or the scale of landscape genetics problems. Thus, an important priority for the field is to develop suitably flexible software tools for graph models that overcome these problems and allow landscape geneticists to explore meaningfully mechanistic and flexible models. We are developing such a library and applying it to examples in landscape genetics.

Landscape genetics combines population genetics, landscape ecology, and spatial analysis to identify landscape and genetic factors that influence genetic and genomic variation. Progress in the field depends on a strong conceptual foundation and the means of identifying mechanistic connnections between environmental factors, landscape features, and genetic or genomic variation. Many existing approaches and much of the software commonly in use was developed for population genetics or statistics and is not entirely appropriate for landscape genetics. Probabilistic graph models provide a statistically rigorous and flexible means of constructing models directly applicable to landscape genetics. Probabilistic graph models also allow construction of mechanistic models, which are crucial elements in testing hypotheses. Sophisticated software exists for the analysis of graph models; however, much of it does not handle the types of data used for landscape genetics, model structures involving autoregressive spatial interaction between variables, or the scale of landscape genetics problems. Thus, an important priority for the field is to develop suitably flexible software tools for graph models that overcome these problems and allow landscape geneticists to explore meaningfully mechanistic and flexible models. We are developing such a library and applying it to examples in landscape genetics.

An Open source GIS-based tool for economic loss estimation due to flood eventshttps://peerj.com/preprints/22282017-09-262017-09-26Simone SterlacchiniMarco ZazzeriGiacomo CappelliniMichele PastormerloAlessandro Bonazzi
Two complementary GIS-based functions are designed and implemented to assess the expected degree of loss due to the occurrence of flood events. Each function processes institutional thematic layers and allows decision makers first to quantify the physical and the economic exposure of the elements at risk in a given study region and then to assess the expected degree of economic loss in relation to the flood water depth chosen for the analysis. The functions are implemented using QGIS with GRASS Python API extension and the workflow is exposed as a QGIS plug-in. The GUI is built over QT multi-platform framework and, therefore, the results are consistently integrated into the QGIS system.

Two complementary GIS-based functions are designed and implemented to assess the expected degree of loss due to the occurrence of flood events. Each function processes institutional thematic layers and allows decision makers first to quantify the physical and the economic exposure of the elements at risk in a given study region and then to assess the expected degree of economic loss in relation to the flood water depth chosen for the analysis. The functions are implemented using QGIS with GRASS Python API extension and the workflow is exposed as a QGIS plug-in. The GUI is built over QT multi-platform framework and, therefore, the results are consistently integrated into the QGIS system.

Mapping irrigated areas using multi-sensor remote sensing data in a Mediterranean environmenthttps://peerj.com/preprints/21552017-09-262017-09-26Pasquale NinoSilvia VaninoFlavio LupiaGuido D'UrsoCarlo De MicheleGiuseppe PuligheGuido Bonati
Water managers need map of irrigated areas (defined as the identification of their location and their areal extent) to plan a rational use of water under limited availability and to prevent the unauthorized withdrawals. Many authors have shown that the Earth Observation techniques are an effective tool for mapping irrigated areas worldwide at different spatial scales (global/regional/and local). This study presents a methodology for mapping irrigated areas in semi-arid environment based on Earth Observation techniques and by fully exploiting datasets freely available processed by open source software and tools. Data acquired with the Landsat 8 Operational Land Imager (OLI) and the new Sentinel 2A MultiSpectral Instrument (MSI) sensors were integrated to obtain cloud free dense time series allowing to monitor the vegetation development throughout the growing seasons. Irrigated areas were identified by analysing the growing patterns under water deficit conditions from NDVI values under the assumption that, in arid and semi-arid environment (like the Mediterranean Region), high trend of vegetation growth are compatible only with irrigation. The method was applied inside the Cixerri Consortium Irrigation District located in South of Sardinia (Italy).

Water managers need map of irrigated areas (defined as the identification of their location and their areal extent) to plan a rational use of water under limited availability and to prevent the unauthorized withdrawals. Many authors have shown that the Earth Observation techniques are an effective tool for mapping irrigated areas worldwide at different spatial scales (global/regional/and local). This study presents a methodology for mapping irrigated areas in semi-arid environment based on Earth Observation techniques and by fully exploiting datasets freely available processed by open source software and tools. Data acquired with the Landsat 8 Operational Land Imager (OLI) and the new Sentinel 2A MultiSpectral Instrument (MSI) sensors were integrated to obtain cloud free dense time series allowing to monitor the vegetation development throughout the growing seasons. Irrigated areas were identified by analysing the growing patterns under water deficit conditions from NDVI values under the assumption that, in arid and semi-arid environment (like the Mediterranean Region), high trend of vegetation growth are compatible only with irrigation. The method was applied inside the Cixerri Consortium Irrigation District located in South of Sardinia (Italy).

Impact of the catchment land use on some factors of lakes trophic status: a GIS approachhttps://peerj.com/preprints/22032017-09-252017-09-25Łukasz SługockiRobert Czerniawski
Background. Artificial enrichment of lakes has posed serious management problems for water supply. In results many European lakes had already undergone significant eutrophication. It seems that a good tool to determine the influence of catchment use on the trophic changes in lakes is Geographic Information System (GIS) and its databases.
Methods. The study covered 31 stratified lakes located in northwestern Poland. These lakes were chosen on account of their considerable recreation value and economic importance. The parameters chosen as dependent variables were Secchi depth and electrical conductivity. Local catchments and network catchment of studied lakes as independent variables were prepared using QGIS Wien (2.8.7). The land use variables were prepared with Corine Land Cover, 2006 (CLC2006).
Results. According to Carlson index the studied lakes ranged from mesotrophic to eutrophic. Both dependent variables Secchi depth and conductivity values were significantly correlated with independent land use variables (P < 0.05).
Discussion. Our survey revealed that percentage use of the catchment (developed in the Geographic Information System) can be a useful tool in the assessment of the lakes risks. With the GIS tools we also confirmed a significant impact of land use on changes transparency and conductivity values in North West lakes in Poland.

Background. Artificial enrichment of lakes has posed serious management problems for water supply. In results many European lakes had already undergone significant eutrophication. It seems that a good tool to determine the influence of catchment use on the trophic changes in lakes is Geographic Information System (GIS) and its databases.

Methods. The study covered 31 stratified lakes located in northwestern Poland. These lakes were chosen on account of their considerable recreation value and economic importance. The parameters chosen as dependent variables were Secchi depth and electrical conductivity. Local catchments and network catchment of studied lakes as independent variables were prepared using QGIS Wien (2.8.7). The land use variables were prepared with Corine Land Cover, 2006 (CLC2006).

Results. According to Carlson index the studied lakes ranged from mesotrophic to eutrophic. Both dependent variables Secchi depth and conductivity values were significantly correlated with independent land use variables (P < 0.05).

Discussion. Our survey revealed that percentage use of the catchment (developed in the Geographic Information System) can be a useful tool in the assessment of the lakes risks. With the GIS tools we also confirmed a significant impact of land use on changes transparency and conductivity values in North West lakes in Poland.

Summer Heat Risk Index: how to integrate recent climatic changes and soil consumption componenthttps://peerj.com/preprints/22342017-09-212017-09-21Alfonso CrisciLuca CongedoMarco MorabitoMichele Munafò
Face to the urban resiliency two major environmental threats are widely recognized: the increasing summer air temperatures and the soil consumption that affects a large number of city in Italy. The work have the goal to present preliminary the actual Heat Summer Risk defined by using Crichton's Risk Triangle (Crichton, 1999) on the second Italian level of administration (ADM2 - Province). For each administrative unit we have considered as hazard layer the most recent trend of summer air temperature assessed (1980-2014); the exposure layer is individuated by the amount of population living in each province and finally as vulnerable layer the mean degree of soil consumption expressed in percentage was considered. Thanks to these information Crichton's methodology are able to give a quantitative risk value index further classified in five risk class. Data sources was provided by several authoritative institutions : (i) ISPRA ( Italian National Institute for Environmental Protection and Research) that provide data about density of soil consumption for 2015 as reported in the Soil Consumption Report 2016; (ii) ECAD (European Climate Assessment \& Dataset) that gives detailed historical daily climatic layers (E-OBS 1950-2015 v 13.0); (iii) ISTAT ( Italian National Institute of Statistics) that provides the last updates on Italian population data (2016). The results was mapped and presented. All computations was carried out in R-STAT environment by using different library available for Spatial and Trend Analysis. Data and code are released in public repository.

Face to the urban resiliency two major environmental threats are widely recognized: the increasing summer air temperatures and the soil consumption that affects a large number of city in Italy. The work have the goal to present preliminary the actual Heat Summer Risk defined by using Crichton's Risk Triangle (Crichton, 1999) on the second Italian level of administration (ADM2 - Province). For each administrative unit we have considered as hazard layer the most recent trend of summer air temperature assessed (1980-2014); the exposure layer is individuated by the amount of population living in each province and finally as vulnerable layer the mean degree of soil consumption expressed in percentage was considered. Thanks to these information Crichton's methodology are able to give a quantitative risk value index further classified in five risk class. Data sources was provided by several authoritative institutions : (i) ISPRA ( Italian National Institute for Environmental Protection and Research) that provide data about density of soil consumption for 2015 as reported in the Soil Consumption Report 2016; (ii) ECAD (European Climate Assessment \& Dataset) that gives detailed historical daily climatic layers (E-OBS 1950-2015 v 13.0); (iii) ISTAT ( Italian National Institute of Statistics) that provides the last updates on Italian population data (2016). The results was mapped and presented. All computations was carried out in R-STAT environment by using different library available for Spatial and Trend Analysis. Data and code are released in public repository.

A redesign of OGC Symbology Encoding standard for sharing cartographyhttps://peerj.com/preprints/24152017-09-122017-09-12Erwan BocherOlivier Ertz
Despite most Spatial Data Infrastructures are offering service-based visualization of geospatial data, requirements are often at a very basic level leading to poor quality of maps. This is a general observation for any geospatial architecture as soon as open standards as those of the Open Geospatial Consortium (OGC) shall be applied. To improve the situation, this paper does focus on improvements at the portrayal interoperability side by considering standardization aspects. We propose two major redesign recommendations. First to consolidate the cartographic theory at the core of the OGC Symbology Encoding standard. Secondly to build the standard in a modular way so as to be ready to be extended with upcoming future cartographic requirements.
Thus, we start by defining portrayal interoperability by means of typical use cases that frame the concept of sharing cartography. Then we bring to light the strengths and limits of the relevant open standards to consider in this context. Finally we propose a set of recommendations to overcome the limits so as to make these use cases a true reality.
Even if the definition of a cartographic-oriented standard is not able to act as a complete cartographic design framework by itself, we argue that pushing forward the standardization work dedicated to cartography is a way to share and disseminate good practices and finally to improve the quality of the visualizations.

Despite most Spatial Data Infrastructures are offering service-based visualization of geospatial data, requirements are often at a very basic level leading to poor quality of maps. This is a general observation for any geospatial architecture as soon as open standards as those of the Open Geospatial Consortium (OGC) shall be applied. To improve the situation, this paper does focus on improvements at the portrayal interoperability side by considering standardization aspects. We propose two major redesign recommendations. First to consolidate the cartographic theory at the core of the OGC Symbology Encoding standard. Secondly to build the standard in a modular way so as to be ready to be extended with upcoming future cartographic requirements.

Thus, we start by defining portrayal interoperability by means of typical use cases that frame the concept of sharing cartography. Then we bring to light the strengths and limits of the relevant open standards to consider in this context. Finally we propose a set of recommendations to overcome the limits so as to make these use cases a true reality.

Even if the definition of a cartographic-oriented standard is not able to act as a complete cartographic design framework by itself, we argue that pushing forward the standardization work dedicated to cartography is a way to share and disseminate good practices and finally to improve the quality of the visualizations.

Assessment of spectral properties of Apollo 12 landing sitehttps://peerj.com/preprints/21242017-09-052017-09-05Yann H CheminIan A CrawfordPeter GrindrodLouise Alexander
The geology and mineralogy of the Apollo 12 landing site has been the subject of recent studies that this research attempts to complement from a remote sensing point of view using the Moon Mineralogy Mapper (M3) sensor data, onboard the Chandrayaan-1 lunar orbiter. It is a higher spatial-spectral resolution sensor than the Clementine UVVis sensor and gives the opportunity to study the lunar surface with a comparatively more detailed spectral resolution.
The M3 signatures are showing a monotonic featureless increment, with very low reflectance, suggesting a mature regolith. The regolith maturity is splitting the landing site in a younger Northwest and older Southeast. The mineral identification using the lunar sample spectra from within the Relab database found some similarity to a basaltic rock/glass mix. The spectrum features of clinopyroxene have been found in the Copernican rays and at the landing site. Lateral mixing increases FeO content away from the central part of the ray. The presence of clinopyroxene in the pigeonite basalt in the stratigraphy of the landing site brings forth some complexity in differentiating the Copernican ray’s clinopyroxene from the local source, as the spectra are twins but for their vertical shift in reflectance, reducing away from the central part of the ray.
Spatial variations in mineralogy were not found mostly because of the pixel size compared to the landing site area. The contribution to stratigraphy is limited to the topmost layer which is a clinopyroxene-dominated basalt belonging to the most remote tip of a Copernican ray and its resulting local regolith mix.

The geology and mineralogy of the Apollo 12 landing site has been the subject of recent studies that this research attempts to complement from a remote sensing point of view using the Moon Mineralogy Mapper (M3) sensor data, onboard the Chandrayaan-1 lunar orbiter. It is a higher spatial-spectral resolution sensor than the Clementine UVVis sensor and gives the opportunity to study the lunar surface with a comparatively more detailed spectral resolution.

The M3 signatures are showing a monotonic featureless increment, with very low reflectance, suggesting a mature regolith. The regolith maturity is splitting the landing site in a younger Northwest and older Southeast. The mineral identification using the lunar sample spectra from within the Relab database found some similarity to a basaltic rock/glass mix. The spectrum features of clinopyroxene have been found in the Copernican rays and at the landing site. Lateral mixing increases FeO content away from the central part of the ray. The presence of clinopyroxene in the pigeonite basalt in the stratigraphy of the landing site brings forth some complexity in differentiating the Copernican ray’s clinopyroxene from the local source, as the spectra are twins but for their vertical shift in reflectance, reducing away from the central part of the ray.

Spatial variations in mineralogy were not found mostly because of the pixel size compared to the landing site area. The contribution to stratigraphy is limited to the topmost layer which is a clinopyroxene-dominated basalt belonging to the most remote tip of a Copernican ray and its resulting local regolith mix.

GIS-based seismic hazard prediction system for urban earthquake disaster prevention planninghttps://peerj.com/preprints/31652017-08-182017-08-18Yongmei ZhaiShenglong ChenQianwen OuYang
A basic framework of a GIS-based seismic hazard prediction system for urban earthquake disaster prevention planning is developed in this study, incorporating structural vulnerability analysis, program development, and GIS. The system is integrated with proven building vulnerability analysis models, data search function, spatial analysis function, and plotting function, realizing the batching and automation of seismic hazard prediction and the interactive visualization of predicted results. The system is applied to a test area and the results are compared with results from previous studies to verify that the system can provide data support and aid decisionmaking for the establishment and implementation of urban earthquake disaster prevention planning. Results from this study are essentially the same as the results of 2003 and slightly better than the results of 1993, which highlights the reliability of the fragility analysis method applied in this system.

A basic framework of a GIS-based seismic hazard prediction system for urban earthquake disaster prevention planning is developed in this study, incorporating structural vulnerability analysis, program development, and GIS. The system is integrated with proven building vulnerability analysis models, data search function, spatial analysis function, and plotting function, realizing the batching and automation of seismic hazard prediction and the interactive visualization of predicted results. The system is applied to a test area and the results are compared with results from previous studies to verify that the system can provide data support and aid decisionmaking for the establishment and implementation of urban earthquake disaster prevention planning. Results from this study are essentially the same as the results of 2003 and slightly better than the results of 1993, which highlights the reliability of the fragility analysis method applied in this system.

Integrating active learning and crowdsourcing into large-scale supervised landcover mapping algorithmshttps://peerj.com/preprints/30042017-06-062017-06-06Stephanie R DebatsLyndon D EstesDavid R ThompsonKelly K Caylor
Sub-Saharan Africa and other developing regions of the world are dominated by smallholder farms, which are characterized by small, heterogeneous, and often indistinct field patterns. In previous work, we developed an algorithm for mapping both smallholder and commercial agricultural fields that includes efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. In this paper, we demonstrated how active learning can be incorporated in the algorithm to create smaller, more efficient training data sets, which reduced computational resources, minimized the need for humans to hand-label data, and boosted performance. We designed a patch-based uncertainty metric to drive the active learning framework, based on the regular grid of a crowdsourcing platform, and demonstrated how subject matter experts can be replaced with fleets of crowdsourcing workers. Our active learning algorithm achieved similar performance as an algorithm trained with randomly selected data, but with 62% less data samples.

Sub-Saharan Africa and other developing regions of the world are dominated by smallholder farms, which are characterized by small, heterogeneous, and often indistinct field patterns. In previous work, we developed an algorithm for mapping both smallholder and commercial agricultural fields that includes efficient extraction of a vast set of simple, highly correlated, and interdependent features, followed by a random forest classifier. In this paper, we demonstrated how active learning can be incorporated in the algorithm to create smaller, more efficient training data sets, which reduced computational resources, minimized the need for humans to hand-label data, and boosted performance. We designed a patch-based uncertainty metric to drive the active learning framework, based on the regular grid of a crowdsourcing platform, and demonstrated how subject matter experts can be replaced with fleets of crowdsourcing workers. Our active learning algorithm achieved similar performance as an algorithm trained with randomly selected data, but with 62% less data samples.